One-Shot Object Detection with Co-Attention and Co-Excitation

11/28/2019
by   Ting-I Hsieh, et al.
15

This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the task is to detect all instances of the same class in a target image. To this end, we develop a novel co-attention and co-excitation (CoAE) framework that makes contributions in three key technical aspects. First, we propose to use the non-local operation to explore the co-attention embodied in each query-target pair and yield region proposals accounting for the one-shot situation. Second, we formulate a squeeze-and-co-excitation scheme that can adaptively emphasize correlated feature channels to help uncover relevant proposals and eventually the target objects. Third, we design a margin-based ranking loss for implicitly learning a metric to predict the similarity of a region proposal to the underlying query, no matter its class label is seen or unseen in training. The resulting model is therefore a two-stage detector that yields a strong baseline on both VOC and MS-COCO under one-shot setting of detecting objects from both seen and never-seen classes. Codes are available at https://github.com/timy90022/One-Shot-Object-Detection.

READ FULL TEXT

page 6

page 7

page 8

research
07/23/2020

Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection

Few-shot object detection aims at detecting objects with few annotated e...
research
08/06/2019

Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector

Conventional methods for object detection usually requires substantial a...
research
04/30/2021

Few-Shot Video Object Detection

We introduce Few-Shot Video Object Detection (FSVOD) with three importan...
research
03/15/2020

OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features

In this paper, we consider the task of one-shot object detection, which ...
research
04/04/2019

Comparison Network for One-Shot Conditional Object Detection

The current advances in object detection depend on large-scale datasets ...
research
01/10/2018

Weakly Supervised One-Shot Detection with Attention Siamese Networks

We consider the task of weakly supervised one-shot detection. In this ta...
research
06/11/2020

Quasi-Dense Instance Similarity Learning

Similarity metrics for instances have drawn much attention, due to their...

Please sign up or login with your details

Forgot password? Click here to reset